Cloud API to Local Inference Migration for Cost & Latency Optimization
Problem statement
Processing sensitive medical records through external, third-party LLM APIs created three compounding problems: exposure of confidential patient data to networks outside the organization's control, unpredictable per-token cost that scaled with usage, and network round-trip latency that made interactive workflows feel sluggish.
My contribution
Model Localization
Deployed a quantized open-source model (Qwen) onto private cloud infrastructure (HPE PCAI), removing any dependency on external inference providers.
Serving Optimization
Benchmarked and tuned local serving runtimes (Ollama and vLLM) to squeeze maximum throughput and minimum latency out of the available hardware.
Privacy Compliance
Re-architected the inference path so that no record ever leaves the internal network, satisfying strict data-privacy requirements for medical data.
Technical stack
Results & impact
- ✓Slashed response latency from 3.6s to 1.1s — a 69% performance lift.
- ✓Achieved 100% compliance with data-privacy regulations by keeping all inference on-premise.
- ✓Eliminated recurring per-token API costs, converting a variable expense into fixed infrastructure.
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